基于渐变凹模圆角半径的高强钢扭曲回弹补偿

doi:10.3901/JME.2019.02.091

机械工程学报 ›› 2019, Vol. 55 ›› Issue (2): 91-97.doi: 10.3901/JME.2019.02.091

基于渐变凹模圆角半径的高强钢扭曲回弹补偿

谢延敏, 张飞, 王子豪, 黄仁勇, 杨俊峰, 胡云川

西南交通大学机械工程学院成都 610031

收稿日期:2018-01-31 修回日期:2018-07-18 出版日期:2019-01-20 发布日期:2019-01-20
通讯作者: 谢延敏(通信作者),男,1975年出生,博士,副教授,硕士研究生导师。主要研究方向为先进塑性加工技术仿真和稳健设计等。E-mail:xie_yanmin@swjtu.edu.cn
基金资助:
国家自然科学基金（51005193）和国家大学生创新性实验计划（201710613033）资助项目

Compensation of Twist Springback in High-strength Steel Based on Gradient Die Radius

XIE Yanmin, ZHANG Fei, WANG Zihao, HUANG Renyong, YANG Junfeng, HU Yunchuan

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031

Received:2018-01-31 Revised:2018-07-18 Online:2019-01-20 Published:2019-01-20

摘要/Abstract

摘要： 为减少高强钢冲压成形扭曲回弹，提出一种基于渐变凹模圆角半径的模具补偿方法。以高强钢TRIP780双C件为研究对象，采用板料冲压成形仿真软件DYNAFORM对该双C件的冲压、扭曲回弹过程进行数值模拟。提出一种评价双C件扭曲回弹程度的指标，并进行双C件扭曲回弹试验，通过三坐标测量仪测量扭曲回弹角，对有限元模型进行了验证。以冲压成形后的扭曲回弹为优化目标，结合相关的工艺参数，利用BP神经网络，基于正交试验，建立凹模圆角半径渐变量、工艺参数与扭曲回弹角之间的网络模型。最后采用遗传算法对该模型迭代寻优获得最优凹模圆角半径渐变量和工艺参数。通过对比优化前后的扭曲回弹角，证明了优化流程有效地减少了双C件扭曲回弹。该方法为扭曲回弹的控制提供了一种新的思路。

关键词: BP神经网络, 高强钢, 渐变凹模圆角半径, 扭曲回弹, 遗传算法

Abstract: In order to reduce the twist springback appearing after the stamping of high-strength steel, a compensation method with gradient die radius is proposed. The double C rail of TRIP780 high-strength steel is taken as the research object. The sheet metal stamping simulation software DYNAFORM is used to numerically simulate the stamping and twist springback processes of the double C rail. An index to evaluate the twist springback of the double C rail is proposed. The experiment of twist springback for the double C rail is carried out. The twist springback angle is measured by means of a three-coordinate measuring instrument, and the finite element model is validated. The twist springback appearing after stamping is taken as the optimization target, and the related process parameters are taken into account. BP neural network is used to establish the network model between the gradient variation of die radius, process parameters and the twist springback angle based on orthogonal test. Finally, the model is iteratively optimized using genetic algorithm to obtain the optimum gradient variation of die radius and process parameters. The twist springback angle is compared before and after the optimization, which proves the optimization flow efficient to reduce the twist springback of the double C rail. This method provides a new way for the control of the twist springback.

Key words: BP neural network, genetic algorithm, gradient die radius, high-strength steel, twist springback

中图分类号:

TG156

谢延敏, 张飞, 王子豪, 黄仁勇, 杨俊峰, 胡云川. 基于渐变凹模圆角半径的高强钢扭曲回弹补偿[J]. 机械工程学报, 2019, 55(2): 91-97.

XIE Yanmin, ZHANG Fei, WANG Zihao, HUANG Renyong, YANG Junfeng, HU Yunchuan. Compensation of Twist Springback in High-strength Steel Based on Gradient Die Radius[J]. Journal of Mechanical Engineering, 2019, 55(2): 91-97.

参考文献

[1] ANDERSSON A. Numerical and experimental evaluation of springback in advanced high strength steel[J]. Journal of Materials Engineering and Performance,2007,16(3):301-307.
[2] 胡康康,彭雄奇,陈军,等. 基于Yoshida-Uemori材料模型的汽车结构件冲压回弹分析[J]. 材料科学与工艺,2011,19(6):43-47. HU Kangkang,PENG Xiongqi,CHEN Jun,et al. Springback prediction of automobile body panel based on Yoshida-Uemori material model[J]. Material Science & Technology,2011,19(6):43-47.
[3] XUE X,LIAO J,VINCZE G,et al. Twist springback characteristics of dual-phase steel sheet after non-axisymmetric deep drawing[J]. International Journal of Material Forming,2015,10(2):267-278.
[4] 郭超群,陈军,陈劼实,等. 超高强度薄板扭曲回弹特性的数值模拟与试验分析[J]. 上海交通大学学报,2010,44(4):468-472. GUO Chaoqun,CHEN Jun,CHEN Jieshi,et al. Numerical simulation and experimental validation of distortional springback of advanced high-strength steel sheet metal forming[J]. Journal of Shanghai Jiaotong University,2010,44(4):468-472.
[5] 黄仁勇,谢延敏,唐维,等. 基于混合硬化模型的TRIP780高强钢双C梁扭曲回弹仿真与试验[J]. 工程设计学报,2017,24(6):668-674. HUANG Renyong,XIE Yanmin,TANG Wei,et al. Experiment and simulation on twist springback for the double C rail of TRIP780 high strength steel based on mixed hardening model[J]. Chinese Journal of Engineering Design,2017,24(6):668-674.
[6] 吴磊,李光耀,曹昭展,等. 高强钢材料性能对汽车零件扭曲回弹的影响[J]. 塑性工程学报,2009,16(3):13-17. WU Lei,LI Guangyao,CAO Zhaozhan,et al. The effect of material properties influenced on torsion springback of high strength steel parts[J]. Journal of Plasticity Engineering,2009,16(3):13-17.
[7] 谢延敏,孙新强,田银,等. 基于改进粒子群算法和小波神经网络的高强钢扭曲回弹工艺参数优化[J]. 机械工程学报,2016,52(19):162-167. XIE Yanmin,SUN Xinqiang,TIAN Yin,et al. Optimization of parameters in twist springback process for high-strength sheets based on improved partical swarm optimization algorithm and neural network[J]. Journal of Mechanical Engineering,2016,52(19):162-167.
[8] 徐正兴,路平,江开勇. 高强度钢冲压成形中扭曲回弹仿真与工艺参数优化[J]. 机械设计与制造,2012(10):262-264. XU Zhengxing,LU Ping,JIANG Kaiyong. Torsion springback simulation and optimization of process parameters of high strength steel in the stamping forming[J]. Machinery Design & Manufacture,2012(10):262-264.
[9] 刘强,阮锋,薛新,等. 三维非规则冲压件的回弹扭转补偿控制[J]. 华南理工大学学报,2009,37(9):93-97. LIU Qiang,RUAN Feng,XUE Xin,et al. Compensation control of springback torsion for 3D irregular stamping parts[J]. Journal of South China University of Technology,2009,37(9):93-97.
[10] 李洪周. 先进高强度钢扭曲回弹研究[D]. 长沙:湖南大学,2009. LI Hongzhou. A study on twist springback of advance high strength steel[D]. Changsha:Hunan University,2009.
[11] BARLAT F,LEGE D J,BREM J C. A six-component yield function for anisotropic materials[J]. International Journal of Plasticity,1991,7(7):693-712.
[12] 张卿卿,李大永,彭颖红,等. TRIP780高强度钢板动态力学特性的研究[J]. 塑性工程学报,2009,16(6):6-10. ZHANG Qingqing,LI Dayong,PENG Yinghong,et al. Research on the dynamic mechanical characteristics of TRIP780 high strength steel sheets[J]. Journal of Plasticity Engineering,2009,16(6):6-10.
[13] 林忠钦,刘罡,李淑慧,等. 应用正交试验设计提高U形件的成形精度[J]. 机械工程学报,2002,38(3):83-89. LIN Zhongqin,LIU Gang,LI Shuhui,et al. Application orthogonal experiment design in increasing dimensional accuracy of U-shaped parts[J]. Chinese Journal of Mechanical Engineering,2002,38(3):83-89.
[14] 姜奎华. 冲压工艺与模具设计[M]. 北京:机械工业出版社,2005. JIANG Kuihua. Pressing technology and die design[M]. Beijing:China Machine Press,2005.
[15] INAMDAR M,DATE P P,NARASIMHAN K,et al. Development of an artificial neural network to predict springback in air vee bending[J]. International Jounal of Advanced Manufacturing Technology,2000,16(5):376-381.
[16] 谢延敏,唐维,黄仁勇,等. 基于SA-RBF神经网络的冲压成形拉延筋优化[J]. 西南交通大学学报,2017,52(5):970-976,993. XIE Yanmin,TANG Wei,HUANG Renyong,et al. Drawbead optimisation in stamping using SA-RBF neural networks[J]. Journal of Southwest Jiaotong University,2017,52(5):970-976,993.
[17] 秦国华,谢文斌,王华敏. 基于神经网络与遗传算法的刀具磨损检测与控制[J]. 光学精密工程,2015,23(5):1314-1321. QIN Guohua,XIE Wenbin,WANG Huamin. Detection and control for tool wear based on neural network and genetic algorithm[J]. Optics and Precision Engineering,2015,23(5):1314-1321.

基于渐变凹模圆角半径的高强钢扭曲回弹补偿

Compensation of Twist Springback in High-strength Steel Based on Gradient Die Radius

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